"""Agents: auto plan agents example?

Examples:

    Execute the following command in the terminal:
    Set env params.
    .. code-block:: shell

        export SILICONFLOW_API_KEY=sk-xx
        export SILICONFLOW_API_BASE=https://xx:80/v1

    run example.
    ..code-block:: shell
        python examples/agents/auto_plan_agent_dialogue_example.py
"""

import asyncio
import os

from dbgpt.agent import (
    AgentContext,
    AgentMemory,
    AutoPlanChatManager,
    LLMConfig,
    UserProxyAgent,
)
from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent
from dbgpt.util.tracer import initialize_tracer

initialize_tracer(
    "/tmp/agent_auto_plan_agent_dialogue_example_trace.jsonl", create_system_app=True
)


async def main():
    from dbgpt.model.proxy.llms.siliconflow import SiliconFlowLLMClient

    llm_client = SiliconFlowLLMClient(
        model_alias=os.getenv(
            "SILICONFLOW_MODEL_VERSION", "Qwen/Qwen2.5-Coder-32B-Instruct"
        ),
    )

    context: AgentContext = AgentContext(
        conv_id="test456", gpts_app_name="代码分析助手", max_new_tokens=2048
    )
    agent_memory = AgentMemory()
    agent_memory.gpts_memory.init(conv_id="test456")
    try:
        coder = (
            await CodeAssistantAgent()
            .bind(context)
            .bind(LLMConfig(llm_client=llm_client))
            .bind(agent_memory)
            .build()
        )

        manager = (
            await AutoPlanChatManager()
            .bind(context)
            .bind(agent_memory)
            .bind(LLMConfig(llm_client=llm_client))
            .build()
        )
        manager.hire([coder])

        user_proxy = await UserProxyAgent().bind(context).bind(agent_memory).build()

        await user_proxy.initiate_chat(
            recipient=manager,
            reviewer=user_proxy,
            message="Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.",
            # message="Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table",
            # message="find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.",
        )
    finally:
        agent_memory.gpts_memory.clear(conv_id="test456")


if __name__ == "__main__":
    ## dbgpt-vis message infos
    asyncio.run(main())
